Inserting probabilistic models in deterministic workflows for robotic process automation and supervisor system

ABSTRACT

Probabilistic models may be used in a deterministic workflow for robotic process automation (RPA). Machine learning (ML) introduces a probabilistic framework where the outcome is not deterministic, and therefore, the steps are not deterministic. Deterministic workflows may be mixed with probabilistic workflows, or probabilistic activities may be inserted into deterministic workflows, in order to create more dynamic workflows. A supervisor system may be used to monitor an ML model and raise an alarm, disable an RPA robot, bypass an RPA robot, or roll back to a previous version of the ML model when an error is detected by a data drift detector, a concept drift detector, or both.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 62/915,434 filed Oct. 15, 2019. The subject matter ofthis earlier filed application is hereby incorporated by reference inits entirety.

FIELD

The present invention generally relates to robotic process automation(RPA), and more specifically, to inserting probabilistic models indeterministic workflows for RPA and a supervisor system.

BACKGROUND

Current RPA systems work in a logistic fashion with decision points in asequential workflow. However, such workflows are based on set logic andare not dynamic. Accordingly, an improved approach may be beneficial.

SUMMARY

Certain embodiments of the present invention may provide solutions tothe problems and needs in the art that have not yet been fullyidentified, appreciated, or solved by current RPA technologies. Forexample, some embodiments of the present invention pertain to insertingprobabilistic models in deterministic workflows for RPA and a supervisorsystem.

In an embodiment, a computer-implemented method for implementingprobabilistic models in a deterministic workflow for RPA includesgenerating a deterministic RPA workflow for RPA robots, generating aplurality of RPA robots configured to execute the deterministic RPAworkflow, and deploying the plurality of RPA robots. Thecomputer-implemented method also includes training a machine learning(ML) model associated with a probabilistic activity to replace adeterministic activity in the RPA workflow.

In another embodiment, a computer-implemented method for implementingprobabilistic models in a deterministic workflow for RPA includesgenerating a plurality of RPA robots configured to execute adeterministic RPA workflow and deploying the plurality of RPA robotsconfigured to execute the deterministic RPA workflow. Thecomputer-implemented method also includes training an ML modelassociated with a probabilistic activity to replace a deterministicactivity in the RPA workflow.

In yet another embodiment, a computer-implemented method includesgenerating an RPA robot configured to execute an RPA workflow includinga probabilistic activity and deploying the RPA robot. Thecomputer-implemented method also includes monitoring, by a supervisorsystem, an ML model called by the probabilistic activity in the RPAworkflow to ensure that the ML model is operating correctly usingmetrics on an input side via a data drift detector and on an output sideusing a concept drift detector.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of certain embodiments of the inventionwill be readily understood, a more particular description of theinvention briefly described above will be rendered by reference tospecific embodiments that are illustrated in the appended drawings.While it should be understood that these drawings depict only typicalembodiments of the invention and are not therefore to be considered tobe limiting of its scope, the invention will be described and explainedwith additional specificity and detail through the use of theaccompanying drawings, in which:

FIG. 1 is an architectural diagram illustrating an RPA system, accordingto an embodiment of the present invention.

FIG. 2 is an architectural diagram illustrating a deployed RPA system,according to an embodiment of the present invention.

FIG. 3 is an architectural diagram illustrating the relationship betweena designer, activities, and drivers, according to an embodiment of thepresent invention.

FIG. 4 is an architectural diagram illustrating an RPA system, accordingto an embodiment of the present invention.

FIG. 5 is an architectural diagram illustrating a computing systemconfigured to use probabilistic models in deterministic workflows forRPA, according to an embodiment of the present invention.

FIG. 6 is a flowchart illustrating a process for implementingprobabilistic models in a deterministic workflow for RPA, according toan embodiment of the present invention.

FIG. 7 is a flowchart illustrating a process for a supervisor system,according to an embodiment of the present invention.

FIG. 8 is an architectural diagram illustrating a supervisor system,according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Some embodiments pertain to using probabilistic models in adeterministic workflow for RPA. Such embodiments may make initialdeployments faster, for example. Machine learning introduces aprobabilistic framework where the outcome is not deterministic, andtherefore, the steps are not deterministic. Deterministic workflows maybe mixed with probabilistic workflows, or probabilistic activities maybe inserted into deterministic workflows, in order to create moredynamic workflows. RPA workflows are usually in the form of logisticflow that is very deterministic with a fixed number of activities.However, probabilistic activities may be inserted in the place of one ormore deterministic activities in the workflow in some embodiments. Toinsert a probabilistic activity, certain factors may be considered, suchas a confidence level of a probabilistic activity. If a machine learning(ML) model associated with a probabilistic activity is trained such thatit becomes accurate enough for use, a probabilistic activity may be usedin the workflow in place of a deterministic activity. Thus, in someembodiments, workflows may begin as deterministic in nature and then bemodified later to be probabilistic.

Supervisor System

Testing probabilistic systems can be difficult, however. Accordingly, insome embodiments, a supervisor system is used to monitor RPA robotsexecuting probabilistic workflows to determine correct operation. Forexample, consider the case of an RPA implementation on an aircraft. Arobot may be trained to perform certain controls, such as causing theaircraft to sacrifice altitude for speed if the robot determines that astall may happen. However, this may not always be the desired actiondepending on flight conditions. For instance, performing this actionshortly after takeoff may cause a crash.

The supervisor system may monitor the actions taken by robots and alsomonitor other data from the system. In the example above, the monitoreddata may include pilot actions. If the supervisor system determines thatsoon after the robot causes the aircraft to dive, the pilots pull backon the wheel to stop the dive. The supervisor system may then disablethe robot or a portion of the robot's workflow and send the collecteddata so the robot can be retrained.

In some embodiments, an ML model may be monitored to ensure it isoperating correctly with metrics on the input side and the output side.For instance, if the ML model requires sixteen data feeds, but one isbroken, the ML model may not be suitable to use. This is monitoring for“data drift”. A data drift detector may be included on the input side.

On the output side, it may be known what ranges to expect. For instance,if a hospital's return rate is known to not have been more than 10%, butit is determined that 290% of patients are coming back, something iswrong. This is called concept drift, which may be monitored by a conceptdrift detector. The supervisor system may thus expect an ML model toperform a certain way and comply with certain parameters. The supervisorsystem may raise an alarm if a data drift detector indicates an error, aconcept drift detector indicates an error, or both.

Consider the case of a supervisor system supervising ML modelsimplemented in an aircraft. If a certain ML model affecting controlsurfaces is run and the nose is dipped aggressively or the descent israpid when the autopilot is engaged, this could be flagged by thesupervisor system, and robots calling the ML models could be shut off orbypassed by manual control. If the robot is not mission critical, or aprevious version of an ML model is known to work with a high confidencein a mission critical system, the supervisor system may roll the MLmodel back to a previous version.

FIG. 1 is an architectural diagram illustrating an RPA system 100,according to an embodiment of the present invention. RPA system 100includes a designer 110 that allows a developer to design and implementworkflows. Designer 110 may provide a solution for applicationintegration, as well as automating third-party applications,administrative Information Technology (IT) tasks, and business ITprocesses. Designer 110 may facilitate development of an automationproject, which is a graphical representation of a business process.Simply put, designer 110 facilitates the development and deployment ofworkflows and robots.

The automation project enables automation of rule-based processes bygiving the developer control of the execution order and the relationshipbetween a custom set of steps developed in a workflow, defined herein as“activities.” One commercial example of an embodiment of designer 110 isUiPath Studio™. Each activity may include an action, such as clicking abutton, reading a file, writing to a log panel, etc. In someembodiments, workflows may be nested or embedded.

Some types of workflows may include, but are not limited to, sequences,flowcharts, Finite State Machines (FSMs), and/or global exceptionhandlers. Sequences may be particularly suitable for linear processes,enabling flow from one activity to another without cluttering aworkflow. Flowcharts may be particularly suitable to more complexbusiness logic, enabling integration of decisions and connection ofactivities in a more diverse manner through multiple branching logicoperators. FSMs may be particularly suitable for large workflows. FSMsmay use a finite number of states in their execution, which aretriggered by a condition (i.e., transition) or an activity. Globalexception handlers may be particularly suitable for determining workflowbehavior when encountering an execution error and for debuggingprocesses.

Once a workflow is developed in designer 110, execution of businessprocesses is orchestrated by conductor 120, which orchestrates one ormore robots 130 that execute the workflows developed in designer 110.One commercial example of an embodiment of conductor 120 is UiPathOrchestrator™. Conductor 120 facilitates management of the creation,monitoring, and deployment of resources in an environment. Conductor 120may act as an integration point with third-party solutions andapplications.

Conductor 120 may manage a fleet of robots 130, connecting and executingrobots 130 from a centralized point. Types of robots 130 that may bemanaged include, but are not limited to, attended robots 132, unattendedrobots 134, development robots (similar to unattended robots 134, butused for development and testing purposes), and nonproduction robots(similar to attended robots 132, but used for development and testingpurposes). Attended robots 132 are triggered by user events and operatealongside a human on the same computing system. Attended robots 132 maybe used with conductor 120 for a centralized process deployment andlogging medium. Attended robots 132 may help the human user accomplishvarious tasks, and may be triggered by user events. In some embodiments,processes cannot be started from conductor 120 on this type of robotand/or they cannot run under a locked screen. In certain embodiments,attended robots 132 can only be started from a robot tray or from acommand prompt. Attended robots 132 should run under human supervisionin some embodiments.

Unattended robots 134 run unattended in virtual environments and canautomate many processes. Unattended robots 134 may be responsible forremote execution, monitoring, scheduling, and providing support for workqueues. Debugging for all robot types may be run in designer 110 in someembodiments. Both attended and unattended robots may automate varioussystems and applications including, but not limited to, mainframes, webapplications, VMs, enterprise applications (e.g., those produced bySAP®, SalesForce®, Oracle®, etc.), and computing system applications(e.g., desktop and laptop applications, mobile device applications,wearable computer applications, etc.).

Conductor 120 may have various capabilities including, but not limitedto, provisioning, deployment, configuration, queueing, monitoring,logging, and/or providing interconnectivity. Provisioning may includecreating and maintenance of connections between robots 130 and conductor120 (e.g., a web application). Deployment may include assuring thecorrect delivery of package versions to assigned robots 130 forexecution. Configuration may include maintenance and delivery of robotenvironments and process configurations. Queueing may include providingmanagement of queues and queue items. Monitoring may include keepingtrack of robot identification data and maintaining user permissions.Logging may include storing and indexing logs to a database (e.g., anSQL database) and/or another storage mechanism (e.g., ElasticSearch®,which provides the ability to store and quickly query large datasets).Conductor 120 may provide interconnectivity by acting as the centralizedpoint of communication for third-party solutions and/or applications.

Robots 130 are execution agents that run workflows built in designer110. One commercial example of some embodiments of robot(s) 130 isUiPath Robots™. In some embodiments, robots 130 install the MicrosoftWindows® Service Control Manager (SCM)-managed service by default. As aresult, such robots 130 can open interactive Windows® sessions under thelocal system account, and have the rights of a Windows® service.

In some embodiments, robots 130 can be installed in a user mode. Forsuch robots 130, this means they have the same rights as the user underwhich a given robot 130 has been installed. This feature may also beavailable for High Density (HD) robots, which ensure full utilization ofeach machine at its maximum potential. In some embodiments, any type ofrobot 130 may be configured in an HD environment.

Robots 130 in some embodiments are split into several components, eachbeing dedicated to a particular automation task. The robot components insome embodiments include, but are not limited to, SCM-managed robotservices, user mode robot services, executors, agents, and command line.SCM-managed robot services manage and monitor Windows® sessions and actas a proxy between conductor 120 and the execution hosts (i.e., thecomputing systems on which robots 130 are executed). These services aretrusted with and manage the credentials for robots 130. A consoleapplication is launched by the SCM under the local system.

User mode robot services in some embodiments manage and monitor Windows®sessions and act as a proxy between conductor 120 and the executionhosts. User mode robot services may be trusted with and manage thecredentials for robots 130. A Windows® application may automatically belaunched if the SCM-managed robot service is not installed.

Executors may run given jobs under a Windows® session (i.e., they mayexecute workflows. Executors may be aware of per-monitor dots per inch(DPI) settings. Agents may be Windows® Presentation Foundation (WPF)applications that display the available jobs in the system tray window.Agents may be a client of the service. Agents may request to start orstop jobs and change settings. The command line is a client of theservice. The command line is a console application that can request tostart jobs and waits for their output.

Having components of robots 130 split as explained above helpsdevelopers, support users, and computing systems more easily run,identify, and track what each component is executing. Special behaviorsmay be configured per component this way, such as setting up differentfirewall rules for the executor and the service. The executor may alwaysbe aware of DPI settings per monitor in some embodiments. As a result,workflows may be executed at any DPI, regardless of the configuration ofthe computing system on which they were created. Projects from designer110 may also be independent of browser zoom level in some embodiments.For applications that are DPI-unaware or intentionally marked asunaware, DPI may be disabled in some embodiments.

FIG. 2 is an architectural diagram illustrating a deployed RPA system200, according to an embodiment of the present invention. In someembodiments, RPA system 200 may be, or may be a part of, RPA system 100of FIG. 1. It should be noted that the client side, the server side, orboth, may include any desired number of computing systems withoutdeviating from the scope of the invention. On the client side, a robotapplication 210 includes executors 212, an agent 214, and a designer216. However, in some embodiments, designer 216 may not be running oncomputing system 210. Executors 212 are running processes. Severalbusiness projects may run simultaneously, as shown in FIG. 2. Agent 214(e.g., a Windows® service) is the single point of contact for allexecutors 212 in this embodiment. All messages in this embodiment arelogged into conductor 230, which processes them further via databaseserver 240, indexer server 250, or both. As discussed above with respectto FIG. 1, executors 212 may be robot components.

In some embodiments, a robot represents an association between a machinename and a username. The robot may manage multiple executors at the sametime. On computing systems that support multiple interactive sessionsrunning simultaneously (e.g., Windows® Server 2012), multiple robots maybe running at the same time, each in a separate Windows® session using aunique username. This is referred to as HD robots above.

Agent 214 is also responsible for sending the status of the robot (e.g.,periodically sending a “heartbeat” message indicating that the robot isstill functioning) and downloading the required version of the packageto be executed. The communication between agent 214 and conductor 230 isalways initiated by agent 214 in some embodiments. In the notificationscenario, agent 214 may open a WebSocket channel that is later used byconductor 230 to send commands to the robot (e.g., start, stop, etc.).

On the server side, a presentation layer (web application 232, Open DataProtocol (OData) Representative State Transfer (REST) ApplicationProgramming Interface (API) endpoints 234, and notification andmonitoring 236), a service layer (API implementation/business logic238), and a persistence layer (database server 240 and indexer server250) are included. Conductor 230 includes web application 232, ODataREST API endpoints 234, notification and monitoring 236, and APIimplementation/business logic 238. In some embodiments, most actionsthat a user performs in the interface of conductor 220 (e.g., viabrowser 220) are performed by calling various APIs. Such actions mayinclude, but are not limited to, starting jobs on robots,adding/removing data in queues, scheduling jobs to run unattended, etc.without deviating from the scope of the invention. Web application 232is the visual layer of the server platform. In this embodiment, webapplication 232 uses Hypertext Markup Language (HTML) and JavaScript(JS). However, any desired markup languages, script languages, or anyother formats may be used without deviating from the scope of theinvention. The user interacts with web pages from web application 232via browser 220 in this embodiment in order to perform various actionsto control conductor 230. For instance, the user may create robotgroups, assign packages to the robots, analyze logs per robot and/or perprocess, start and stop robots, etc.

In addition to web application 232, conductor 230 also includes servicelayer that exposes OData REST API endpoints 234. However, otherendpoints may be included without deviating from the scope of theinvention. The REST API is consumed by both web application 232 andagent 214. Agent 214 is the supervisor of one or more robots on theclient computer in this embodiment.

The REST API in this embodiment covers configuration, logging,monitoring, and queueing functionality. The configuration endpoints maybe used to define and configure application users, permissions, robots,assets, releases, and environments in some embodiments. Logging RESTendpoints may be used to log different information, such as errors,explicit messages sent by the robots, and other environment-specificinformation, for instance. Deployment REST endpoints may be used by therobots to query the package version that should be executed if the startjob command is used in conductor 230. Queueing REST endpoints may beresponsible for queues and queue item management, such as adding data toa queue, obtaining a transaction from the queue, setting the status of atransaction, etc.

Monitoring REST endpoints may monitor web application 232 and agent 214.Notification and monitoring API 236 may be REST endpoints that are usedfor registering agent 214, delivering configuration settings to agent214, and for sending/receiving notifications from the server and agent214. Notification and monitoring API 236 may also use WebSocketcommunication in some embodiments.

The persistence layer includes a pair of servers in thisembodiment—database server 240 (e.g., a SQL server) and indexer server250. Database server 240 in this embodiment stores the configurations ofthe robots, robot groups, associated processes, users, roles, schedules,etc. This information is managed through web application 232 in someembodiments. Database server 240 may manages queues and queue items. Insome embodiments, database server 240 may store messages logged by therobots (in addition to or in lieu of indexer server 250).

Indexer server 250, which is optional in some embodiments, stores andindexes the information logged by the robots. In certain embodiments,indexer server 250 may be disabled through configuration settings. Insome embodiments, indexer server 250 uses ElasticSearch®, which is anopen source project full-text search engine. Messages logged by robots(e.g., using activities like log message or write line) may be sentthrough the logging REST endpoint(s) to indexer server 250, where theyare indexed for future utilization.

FIG. 3 is an architectural diagram illustrating the relationship 300between a designer 310, activities 320, 330, and drivers 340, accordingto an embodiment of the present invention. Per the above, a developeruses designer 310 to develop workflows that are executed by robots.Workflows may include user-defined activities 320 and UI automationactivities 330. Some embodiments are able to identify non-textual visualcomponents in an image, which is called computer vision (CV) herein.Some CV activities pertaining to such components may include, but arenot limited to, click, type, get text, hover, element exists, refreshscope, highlight, etc. Click in some embodiments identifies an elementusing CV, optical character recognition (OCR), fuzzy text matching, andmulti-anchor, for example, and clicks it. Type may identify an elementusing the above and types in the element. Get text may identify thelocation of specific text and scan it using OCR. Hover may identify anelement and hover over it. Element exists may check whether an elementexists on the screen using the techniques described above. In someembodiments, there may be hundreds or even thousands of activities thatcan be implemented in designer 310. However, any number and/or type ofactivities may be available without deviating from the scope of theinvention.

UI automation activities 330 are a subset of special, lower levelactivities that are written in lower level code (e.g., CV activities)and facilitate interactions with the screen. UI automation activities330 facilitate these interactions via drivers 340 that allow the robotto interact with the desired software. For instance, drivers 340 mayinclude OS drivers 342, browser drivers 344, VM drivers 346, enterpriseapplication drivers 348, etc.

Drivers 340 may interact with the OS at a low level looking for hooks,monitoring for keys, etc. They may facilitate integration with Chrome®,IE®, Citrix®, SAP®, etc. For instance, the “click” activity performs thesame role in these different applications via drivers 340.

FIG. 4 is an architectural diagram illustrating an RPA system 400,according to an embodiment of the present invention. In someembodiments, RPA system 400 may be or include RPA systems 100 and/or 200of FIGS. 1 and/or 2. RPA system 400 includes multiple client computingsystems 410 running robots. Computing systems 410 are able tocommunicate with a conductor computing system 420 via a web applicationrunning thereon. Conductor computing system 420, in turn, is able tocommunicate with a database server 430 and an optional indexer server440.

With respect to FIGS. 1 and 3, it should be noted that while a webapplication is used in these embodiments, any suitable client/serversoftware may be used without deviating from the scope of the invention.For instance, the conductor may run a server-side application thatcommunicates with non-web-based client software applications on theclient computing systems.

FIG. 5 is an architectural diagram illustrating a computing system 500configured to use probabilistic models in deterministic workflows forRPA, according to an embodiment of the present invention. In someembodiments, computing system 500 may be one or more of the computingsystems depicted and/or described herein. Computing system 500 includesa bus 505 or other communication mechanism for communicatinginformation, and processor(s) 510 coupled to bus 505 for processinginformation. Processor(s) 510 may be any type of general or specificpurpose processor, including a Central Processing Unit (CPU), anApplication Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA), a Graphics Processing Unit (GPU), multiple instancesthereof, and/or any combination thereof. Processor(s) 510 may also havemultiple processing cores, and at least some of the cores may beconfigured to perform specific functions. Multi-parallel processing maybe used in some embodiments. In certain embodiments, at least one ofprocessor(s) 510 may be a neuromorphic circuit that includes processingelements that mimic biological neurons. In some embodiments,neuromorphic circuits may not require the typical components of a VonNeumann computing architecture.

Computing system 500 further includes a memory 515 for storinginformation and instructions to be executed by processor(s) 510. Memory515 can be comprised of any combination of Random Access Memory (RAM),Read Only Memory (ROM), flash memory, cache, static storage such as amagnetic or optical disk, or any other types of non-transitorycomputer-readable media or combinations thereof. Non-transitorycomputer-readable media may be any available media that can be accessedby processor(s) 510 and may include volatile media, non-volatile media,or both. The media may also be removable, non-removable, or both.

Additionally, computing system 500 includes a communication device 520,such as a transceiver, to provide access to a communications network viaa wireless and/or wired connection. In some embodiments, communicationdevice 520 may be configured to use Frequency Division Multiple Access(FDMA), Single Carrier FDMA (SC-FDMA), Time Division Multiple Access(TDMA), Code Division Multiple Access (CDMA), Orthogonal FrequencyDivision Multiplexing (OFDM), Orthogonal Frequency Division MultipleAccess (OFDMA), Global System for Mobile (GSM) communications, GeneralPacket Radio Service (GPRS), Universal Mobile Telecommunications System(UMTS), cdma2000, Wideband CDMA (W-CDMA), High-Speed Downlink PacketAccess (HSDPA), High-Speed Uplink Packet Access (HSUPA), High-SpeedPacket Access (HSPA), Long Term Evolution (LTE), LTE Advanced (LTE-A),802.11x, Wi-Fi, Zigbee, Ultra-WideBand (UWB), 802.16x, 802.15, HomeNode-B (HnB), Bluetooth, Radio Frequency Identification (RFID), InfraredData Association (IrDA), Near-Field Communications (NFC), fifthgeneration (5G), New Radio (NR), any combination thereof, and/or anyother currently existing or future-implemented communications standardand/or protocol without deviating from the scope of the invention. Insome embodiments, communication device 520 may include one or moreantennas that are singular, arrayed, phased, switched, beamforming,beamsteering, a combination thereof, and or any other antennaconfiguration without deviating from the scope of the invention.

Processor(s) 510 are further coupled via bus 505 to a display 525, suchas a plasma display, a Liquid Crystal Display (LCD), a Light EmittingDiode (LED) display, a Field Emission Display (FED), an Organic LightEmitting Diode (OLED) display, a flexible OLED display, a flexiblesubstrate display, a projection display, a 4K display, a high definitiondisplay, a Retina® display, an In-Plane Switching (IPS) display, or anyother suitable display for displaying information to a user. Display 525may be configured as a touch (haptic) display, a three dimensional (3D)touch display, a multi-input touch display, a multi-touch display, etc.using resistive, capacitive, surface-acoustic wave (SAW) capacitive,infrared, optical imaging, dispersive signal technology, acoustic pulserecognition, frustrated total internal reflection, etc. Any suitabledisplay device and haptic I/O may be used without deviating from thescope of the invention.

A keyboard 530 and a cursor control device 535, such as a computermouse, a touchpad, etc., are further coupled to bus 505 to enable a userto interface with computing system. However, in certain embodiments, aphysical keyboard and mouse may not be present, and the user mayinteract with the device solely through display 525 and/or a touchpad(not shown). Any type and combination of input devices may be used as amatter of design choice. In certain embodiments, no physical inputdevice and/or display is present. For instance, the user may interactwith computing system 500 remotely via another computing system incommunication therewith, or computing system 500 may operateautonomously.

Memory 515 stores software modules that provide functionality whenexecuted by processor(s) 510. The modules include an operating system540 for computing system 500. The modules further include aprobabilistic model module 545 that is configured to perform all or partof the processes described herein or derivatives thereof. Computingsystem 500 may include one or more additional functional modules 550that include additional functionality.

One skilled in the art will appreciate that a “system” could be embodiedas a server, an embedded computing system, a personal computer, aconsole, a personal digital assistant (PDA), a cell phone, a tabletcomputing device, a quantum computing system, or any other suitablecomputing device, or combination of devices without deviating from thescope of the invention. Presenting the above-described functions asbeing performed by a “system” is not intended to limit the scope of thepresent invention in any way, but is intended to provide one example ofthe many embodiments of the present invention. Indeed, methods, systems,and apparatuses disclosed herein may be implemented in localized anddistributed forms consistent with computing technology, including cloudcomputing systems.

It should be noted that some of the system features described in thisspecification have been presented as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom verylarge scale integration (VLSI) circuits or gate arrays, off-the-shelfsemiconductors such as logic chips, transistors, or other discretecomponents. A module may also be implemented in programmable hardwaredevices such as field programmable gate arrays, programmable arraylogic, programmable logic devices, graphics processing units, or thelike.

A module may also be at least partially implemented in software forexecution by various types of processors. An identified unit ofexecutable code may, for instance, include one or more physical orlogical blocks of computer instructions that may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified module need not be physically locatedtogether, but may include disparate instructions stored in differentlocations that, when joined logically together, comprise the module andachieve the stated purpose for the module. Further, modules may bestored on a computer-readable medium, which may be, for instance, a harddisk drive, flash device, RAM, tape, and/or any other suchnon-transitory computer-readable medium used to store data withoutdeviating from the scope of the invention.

Indeed, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set, or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork.

FIG. 6 is a flowchart illustrating a process 600 for implementingprobabilistic models in a deterministic workflow for RPA, according toan embodiment of the present invention. The process begins withgenerating a deterministic RPA workflow for RPA robots at 610. RPArobots configured to execute the deterministic RPA workflow aregenerated at 620. The RPA robots are then deployed at 630.

An ML model associated with a probabilistic activity to replace adeterministic activity in the deterministic RPA workflow is trained at640. Data is collected from RPA robots implementing the deterministicworkflow, their associated computing systems, or both, at 650. Forinstance, the data may include, but is not limited to, operationsundertaken by the robots, user interactions with the computing systems(e.g., buttons pressed, mouse movements, applications used, etc.),processes run by the computing systems, corrections made by users, etc.This data is then used to periodically retrain the ML model until adesired confidence threshold is achieved at 660. A probabilisticworkflow including the probabilistic activity that replaces thedeterministic activity is then generated at 670 and RPA robotsimplementing the workflow are generated and deployed at 680.

FIG. 7 is a flowchart illustrating a process 700 for a supervisorsystem, according to an embodiment of the present invention. In someembodiments, process 700 may be implemented via supervisor system 810 ofFIG. 8. The process begins (or continues from FIG. 6) with a supervisorsystem monitoring an ML model called by a probabilistic activity in anRPA workflow at 710 to ensure that the ML model is operating correctlyusing metrics on an input side via a data drift detector and on anoutput side using a concept drift detector. In some embodiments, thesupervisor system may monitor multiple ML models. In certainembodiments, the concept drift detector determines whether the outputfalls within a predetermined range.

When no error is detected at 720, the monitoring continues at 710.However, when the data drift detector, the concept drift detector, orboth, indicate an error, an alarm is raised at 730 (e.g., sending amessage to a computing system or a robot that an error has beendetected). The supervisor system then takes remedial action at 740. Theremedial action may include, but is not limited to, disabling an RPArobot, bypassing the RPA robot, rolling back to a previous version ofthe ML model, etc.

FIG. 8 is an architectural diagram 800 illustrating operation of asupervisor system 810, according to an embodiment of the presentinvention. Supervisor system includes a data drift detector thatmonitors input to an ML model 820 and a concept drift detector 814 thatmonitors output from ML model 820. RPA robot 830 calls ML model 820.When supervisor system 810 receives an error from data drift detector812 and/or concept drift detector 814, supervisor system 810 may disableor bypass RPA robot 830, generate an alarm, etc.

The process steps performed in FIGS. 6 and 7 may be performed by acomputer program, encoding instructions for the processor(s) to performat least part of the process(es) described in FIGS. 6 and 7, inaccordance with embodiments of the present invention. The computerprogram may be embodied on a non-transitory computer-readable medium.The computer-readable medium may be, but is not limited to, a hard diskdrive, a flash device, RAM, a tape, and/or any other such medium orcombination of media used to store data. The computer program mayinclude encoded instructions for controlling processor(s) of a computingsystem (e.g., processor(s) 510 of computing system 500 of FIG. 5) toimplement all or part of the process steps described in FIGS. 6 and 7,which may also be stored on the computer-readable medium.

The computer program can be implemented in hardware, software, or ahybrid implementation. The computer program can be composed of modulesthat are in operative communication with one another, and which aredesigned to pass information or instructions to display. The computerprogram can be configured to operate on a general purpose computer, anASIC, or any other suitable device.

It will be readily understood that the components of various embodimentsof the present invention, as generally described and illustrated in thefigures herein, may be arranged and designed in a wide variety ofdifferent configurations. Thus, the detailed description of theembodiments of the present invention, as represented in the attachedfigures, is not intended to limit the scope of the invention as claimed,but is merely representative of selected embodiments of the invention.

The features, structures, or characteristics of the invention describedthroughout this specification may be combined in any suitable manner inone or more embodiments. For example, reference throughout thisspecification to “certain embodiments,” “some embodiments,” or similarlanguage means that a particular feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment of the present invention. Thus, appearances of the phrases“in certain embodiments,” “in some embodiment,” “in other embodiments,”or similar language throughout this specification do not necessarily allrefer to the same group of embodiments and the described features,structures, or characteristics may be combined in any suitable manner inone or more embodiments.

It should be noted that reference throughout this specification tofeatures, advantages, or similar language does not imply that all of thefeatures and advantages that may be realized with the present inventionshould be or are in any single embodiment of the invention. Rather,language referring to the features and advantages is understood to meanthat a specific feature, advantage, or characteristic described inconnection with an embodiment is included in at least one embodiment ofthe present invention. Thus, discussion of the features and advantages,and similar language, throughout this specification may, but do notnecessarily, refer to the same embodiment.

Furthermore, the described features, advantages, and characteristics ofthe invention may be combined in any suitable manner in one or moreembodiments. One skilled in the relevant art will recognize that theinvention can be practiced without one or more of the specific featuresor advantages of a particular embodiment. In other instances, additionalfeatures and advantages may be recognized in certain embodiments thatmay not be present in all embodiments of the invention.

One having ordinary skill in the art will readily understand that theinvention as discussed above may be practiced with steps in a differentorder, and/or with hardware elements in configurations which aredifferent than those which are disclosed. Therefore, although theinvention has been described based upon these preferred embodiments, itwould be apparent to those of skill in the art that certainmodifications, variations, and alternative constructions would beapparent, while remaining within the spirit and scope of the invention.

In an embodiment, a computer-implemented method includes generating adeterministic RPA workflow and training an ML model associated with aprobabilistic activity to replace a deterministic activity in thedeterministic RPA workflow. The computer-implemented method alsoincludes collecting data from RPA robots implementing the deterministicworkflow and their associated computing systems and using the collecteddata to periodically retrain the ML model until a confidence thresholdis achieved. Then, the computer-implemented method includes generating aprobabilistic workflow including the probabilistic activity, replacingthe deterministic activity.

The invention claimed is:
 1. A computer-implemented method forimplementing probabilistic models in a deterministic workflow forrobotic process automation (RPA), comprising: generating a deterministicRPA workflow comprising a deterministic activity, by an RPA designerapplication; generating an RPA robot configured to execute thedeterministic RPA workflow, by the RPA designer application; collectingdata from a plurality of deployed RPA robots executing the deterministicRPA workflow, collecting data from computing systems on which thedeployed RPA robots are running, or both; training a machine learning(ML) model associated with a probabilistic activity to replace thedeterministic activity in the RPA workflow; and replacing thedeterministic activity with the probabilistic activity, by the RPAdesigner application.
 2. The computer-implemented method of claim 1,further comprising: periodically retraining the ML model using thecollected data.
 3. The computer-implemented method of claim 2, whereinthe replacing of the deterministic activity in the RPA workflow with theprobabilistic activity occurs after the ML model achieves a confidencethreshold.
 4. The computer-implemented method of claim 3, furthercomprising: generating a plurality of RPA robots configured to executethe RPA workflow comprising the probabilistic activity; and deployingthe plurality of RPA robots that are configured to execute the RPAworkflow comprising the probabilistic activity.
 5. Thecomputer-implemented method of claim 4, further comprising: monitoringthe ML model to ensure that the ML model is operating correctly usingmetrics on an input side via a data drift detector and on an output sideusing a concept drift detector.
 6. The computer-implemented method ofclaim 5, wherein the concept drift detector determines whether theoutput falls within a predetermined range.
 7. The computer-implementedmethod of claim 5, further comprising: raising an alarm when the datadrift detector, the concept drift detector, or both, indicate an error.8. The computer-implemented method of claim 5, wherein when the datadrift detector, the concept drift detector, or both, indicate an error,the method further comprises: disabling or bypassing an RPA robot of theplurality of RPA robots.
 9. The computer-implemented method of claim 5,wherein when the data drift detector, the concept drift detector, orboth, indicate an error, the method further comprises: rolling back to aprevious version of the ML model.
 10. A computer-implemented method forimplementing probabilistic models in a deterministic workflow forrobotic process automation (RPA), comprising: generating a plurality ofRPA robots configured to execute a deterministic RPA workflow comprisinga deterministic activity, by the RPA designer application; deploying theplurality of RPA robots, by the RPA designer application; collectingdata from a plurality of deployed RPA robots executing the deterministicRPA workflow, collecting data from computing systems on which thedeployed RPA robots are running, or both; training a machine learning(ML) model associated with a probabilistic activity to replace thedeterministic activity in the RPA workflow; and replacing thedeterministic activity with the probabilistic activity, by the RPAdesigner application.
 11. The computer-implemented method of claim 10,further comprising: periodically retraining the ML model using thecollected data, wherein the deterministic activity in the RPA workflowis replaced with the probabilistic activity after the ML model achievesa confidence threshold.
 12. The computer-implemented method of claim 11,further comprising: generating a plurality of RPA robots configured toexecute the RPA workflow comprising the probabilistic activity; anddeploying the plurality of RPA robots that are configured to execute theRPA workflow comprising the probabilistic activity.
 13. Thecomputer-implemented method of claim 12, further comprising: monitoringthe ML model to ensure that the ML model is operating correctly usingmetrics on an input side via a data drift detector and on an output sideusing a concept drift detector.
 14. The computer-implemented method ofclaim 13, wherein when the data drift detector, the concept driftdetector, or both, indicate an error, the method further comprises:disabling an RPA robot of the plurality of RPA robots, bypassing the RPArobot of the plurality of RPA robots, or rolling back to a previousversion of the ML model.
 15. A computer-implemented method, comprising:generating a robotic process automation (RPA) robot configured toexecute an RPA workflow comprising a probabilistic activity, by an RPAdesigner application; deploying the RPA robot; monitoring, by asupervisor system, a machine learning (ML) model called by theprobabilistic activity in the RPA workflow, the supervisor systemconfigured to ensure that the ML model is operating correctly usingmetrics on an input side via a data drift detector and on an output sideusing a concept drift detector; and raising an alarm, by the supervisorsystem, when the data drift detector, the concept drift detector, orboth, indicate an error.
 16. The computer-implemented method of claim15, wherein the concept drift detector determines whether the outputfalls within a predetermined range.
 17. The computer-implemented methodof claim 15, wherein when the data drift detector, the concept driftdetector, or both, indicate an error, the method further comprises:disabling an RPA robot of the plurality of RPA robots, bypassing the RPArobot of the plurality of RPA robots, or rolling back to a previousversion of the ML model, by the supervisor system.